论文标题
自适应操作员选择离散参数的统一框架
Unified Framework for the Adaptive Operator Selection of Discrete Parameters
论文作者
论文摘要
我们对进化算法（EAS）中的自适应选择（AOS）进行了详尽的调查。我们通过在AOS方法的现有分类中构建的框架中添加更多组件来简化AOS结构。除了简化，我们还研究了从文献概括的AOS方法之间的共同点。每个组件都有多种替代选择，每个选择都用公式表示。我们进行三组比较。首先，在BBOB测试床上测试了文献中的方法，并具有默认的超级参数。其次，这些方法的超级参数使用称为IRACE的离线配置器调整。第三，对于给定的一组问题，我们使用IRACE选择组件的最佳组合并调整其超级参数。
We conduct an exhaustive survey of adaptive selection of operators (AOS) in Evolutionary Algorithms (EAs). We simplified the AOS structure by adding more components to the framework to built upon the existing categorisation of AOS methods. In addition to simplifying, we looked at the commonality among AOS methods from literature to generalise them. Each component is presented with a number of alternative choices, each represented with a formula. We make three sets of comparisons. First, the methods from literature are tested on the BBOB test bed with their default hyper parameters. Second, the hyper parameters of these methods are tuned using an offline configurator known as IRACE. Third, for a given set of problems, we use IRACE to select the best combination of components and tune their hyper parameters.
